Collaborative filtering recommendation algorithm based on item attributes

Abstract

Aiming at the shortcomings of datasets sparsity and cold start in the traditional Item-based collaborative filtering recommendation algorithm, to improve the calculating accuracy of similarity and recommendation quality, taking attribute theory as theoretical basis, a collaborative filtering recommendation algorithm based on item attributes is proposed. Through analyzing the items, the attributes are listed and attribute weights are calculated, the similarity between items is calculated by taking advantage of attribute barycenter coordinate model and item attribute weights, and then produce recommendations forecasts. Finally, the experimental results show that the compared with traditional algorithm the proposed algorithm can effectively alleviate the user rating data sparsity problem and improve the quality of recommendation system.